APA 6th Edition Turk, R., Peruš, I. & Knap, M. (2002). Modeling and the Reliability of Calculated Flow Curves. Metalurgija, 41 (1), 23-28. Retrieved from https://hrcak.srce.hr/128602
MLA 8th Edition Turk, R., et al. "Modeling and the Reliability of Calculated Flow Curves." Metalurgija, vol. 41, no. 1, 2002, pp. 23-28. https://hrcak.srce.hr/128602. Accessed 16 Oct. 2019.
Chicago 17th Edition Turk, R., I. Peruš and M. Knap. "Modeling and the Reliability of Calculated Flow Curves." Metalurgija 41, no. 1 (2002): 23-28. https://hrcak.srce.hr/128602
Harvard Turk, R., Peruš, I., and Knap, M. (2002). 'Modeling and the Reliability of Calculated Flow Curves', Metalurgija, 41(1), pp. 23-28. Available at: https://hrcak.srce.hr/128602 (Accessed 16 October 2019)
Vancouver Turk R, Peruš I, Knap M. Modeling and the Reliability of Calculated Flow Curves. Metalurgija [Internet]. 2002 [cited 2019 October 16];41(1):23-28. Available from: https://hrcak.srce.hr/128602
IEEE R. Turk, I. Peruš and M. Knap, "Modeling and the Reliability of Calculated Flow Curves", Metalurgija, vol.41, no. 1, pp. 23-28, 2002. [Online]. Available: https://hrcak.srce.hr/128602. [Accessed: 16 October 2019]
Abstracts Flow curves are very important input data for numerical modelling of industrial processes and for direct industrial applications. Precise thermal and mechanical testing of low carbon silicon steel showed obvious differences in yield stresses according to permissible oscillations of chemical composition. Since conventional Hajduk, Elfmark and Spittel equations for flow curve calculation are very rigid and cannot describe the local changes of yield stresses caused by phase transformations, a new neural network aproach for modelling the physical phenomena in materials science has been developed. The obtained results showed that neural-network method is a powerful tool, and it can be applied directly in solving problems of materials science (e.g. materials testing support, mathematical simulation of materials forming process).